Group_10-DistilBERT.ipynb

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      "cell_type": "code",
      "metadata": {
        "id": "oEpX7ZOIAhe5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f14086af-e797-4179-ed89-8c2660cd9faf"
      },
      "source": [
        "# Installing the transformers for use of DistilBERT\n",
        "!pip install transformers"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: transformers in /usr/local/lib/python3.7/dist-packages (4.9.1)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\n",
            "Requirement already satisfied: tokenizers<0.11,>=0.10.1 in /usr/local/lib/python3.7/dist-packages (from transformers) (0.10.3)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers) (3.0.12)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.7/dist-packages (from transformers) (5.4.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2019.12.20)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.41.1)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.19.5)\n",
            "Requirement already satisfied: huggingface-hub==0.0.12 in /usr/local/lib/python3.7/dist-packages (from transformers) (0.0.12)\n",
            "Requirement already satisfied: sacremoses in /usr/local/lib/python3.7/dist-packages (from transformers) (0.0.45)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers) (4.6.1)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from transformers) (21.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from huggingface-hub==0.0.12->transformers) (3.7.4.3)\n",
            "Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->transformers) (2.4.7)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers) (3.5.0)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2021.5.30)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (7.1.2)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.15.0)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.0.1)\n"
          ],
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        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nHezFQ0n89vS"
      },
      "source": [
        "# Import the libraries\n",
        "from google.colab import drive\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from transformers import DistilBertTokenizerFast\n",
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "from transformers import TFDistilBertForSequenceClassification, TFTrainer, TFTrainingArguments"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OVfofxNz_lO9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f21ce5b0-4ccf-4213-8145-b652cecfa935"
      },
      "source": [
        "# Mount the google drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xUedKW_S_NIk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d3d5b55b-7d03-4969-9511-d6e1a1cf70cc"
      },
      "source": [
        "# Import the train data\n",
        "df = pd.read_csv('/content/drive/MyDrive/NLP/train.csv')\n",
        "\n",
        "# Check for the null values\n",
        "print(df.isnull().any(axis = 0))"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "id                   False\n",
            "text                 False\n",
            "is_humor             False\n",
            "humor_rating          True\n",
            "humor_controversy     True\n",
            "offense_rating       False\n",
            "dtype: bool\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P3m-MNyBAJ9m",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a8dc3ee5-ce36-4876-9565-a5a510964f7a"
      },
      "source": [
        "# Checking for shape of the dataset\n",
        "df.shape"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8000, 6)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s6f_WtZp8Abu"
      },
      "source": [
        "## **TASK1(A)** Humor detection"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uMQ_FPNTAMTf"
      },
      "source": [
        "# Train feature\n",
        "X_train=list(df['text'])"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3kUWo3yxAQX8"
      },
      "source": [
        "# Train Label\n",
        "y_train=list(df['is_humor'])"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qxQ4AaM5Aakb"
      },
      "source": [
        "# Spliting the data into training and testing the dataset\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_train, y_train, test_size = 0.20, random_state = 0)"
      ],
      "execution_count": 17,
      "outputs": []
    },
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      "cell_type": "code",
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        "id": "aw-BHsWgAj8f",
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        },
        "outputId": "55caa921-3730-4aab-8a20-ee03920c825a"
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      "source": [
        "# DistilBertTokenizerFast for tokenize the text\n",
        "tokenizer = DistilBertTokenizerFast.from_pretrained('distilbert-base-cased')"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
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        {
          "output_type": "stream",
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            "\n"
          ],
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        },
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          "output_type": "stream",
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          ],
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            "\n"
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          "name": "stdout"
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        {
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          "output_type": "stream",
          "text": [
            "\n"
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ukq4LLeKA3bs"
      },
      "source": [
        "# Encode the text into id vector with truncation and padding\n",
        "train_encodings = tokenizer(X_train, truncation=True, padding=True)\n",
        "test_encodings = tokenizer(X_test, truncation=True, padding=True)"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "23woFuokA7NZ"
      },
      "source": [
        "# Making the training dataset using tensorflow\n",
        "\n",
        "train_dataset = tf.data.Dataset.from_tensor_slices((\n",
        "    dict(train_encodings),\n",
        "    y_train\n",
        "))\n",
        "\n",
        "test_dataset = tf.data.Dataset.from_tensor_slices((\n",
        "    dict(test_encodings),\n",
        "    y_test\n",
        "))"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nL8xJxVeBEN1"
      },
      "source": [
        "# Making Training Argument for model training\n",
        "training_args = TFTrainingArguments(\n",
        "    output_dir='./results_is_humor',          \n",
        "    num_train_epochs=2,              \n",
        "    per_device_train_batch_size=8,  \n",
        "    per_device_eval_batch_size=16,   \n",
        "    warmup_steps=500,                \n",
        "    weight_decay=0.01,               \n",
        "    logging_dir='./logs_is_humor',            \n",
        "    logging_steps=10,\n",
        "    eval_steps=16\n",
        ")"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "id": "gIc5y-IZBXqW",
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      "source": [
        "# Use 'distilbert-base-uncased' as pre-trained model to get weights \n",
        "with training_args.strategy.scope():\n",
        "    model = TFDistilBertForSequenceClassification.from_pretrained(\"distilbert-base-uncased\")\n",
        "\n",
        "trainer = TFTrainer(\n",
        "    model=model,                         \n",
        "    args=training_args,                  \n",
        "    train_dataset=train_dataset,         \n",
        "    eval_dataset=test_dataset             \n",
        ")\n",
        "\n",
        "trainer.train()"
      ],
      "execution_count": 22,
      "outputs": [
        {
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=442.0, style=ProgressStyle(description_…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "65de5cbc3e024861bf26637859144f1b",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=363423424.0, style=ProgressStyle(descri…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Some layers from the model checkpoint at distilbert-base-uncased were not used when initializing TFDistilBertForSequenceClassification: ['activation_13', 'vocab_layer_norm', 'vocab_transform', 'vocab_projector']\n",
            "- This IS expected if you are initializing TFDistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing TFDistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some layers of TFDistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['dropout_19', 'pre_classifier', 'classifier']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/trainer_tf.py:114: FutureWarning: The class `TFTrainer` is deprecated and will be removed in version 5 of Transformers. We recommend using native Keras instead, by calling methods like `fit()` and `predict()` directly on the model object. Detailed examples of the Keras style can be found in our examples at https://github.com/huggingface/transformers/tree/master/examples/tensorflow\n",
            "  FutureWarning,\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:The parameters `output_attentions`, `output_hidden_states` and `use_cache` cannot be updated when calling a model.They have to be set to True/False in the config object (i.e.: `config=XConfig.from_pretrained('name', output_attentions=True)`).\n",
            "WARNING:tensorflow:AutoGraph could not transform <bound method Socket.send of <zmq.sugar.socket.Socket object at 0x7f71ca8a9d70>> and will run it as-is.\n",
            "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
            "Cause: module, class, method, function, traceback, frame, or code object was expected, got cython_function_or_method\n",
            "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
            "WARNING: AutoGraph could not transform <bound method Socket.send of <zmq.sugar.socket.Socket object at 0x7f71ca8a9d70>> and will run it as-is.\n",
            "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
            "Cause: module, class, method, function, traceback, frame, or code object was expected, got cython_function_or_method\n",
            "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
            "WARNING:tensorflow:AutoGraph could not transform <function wrap at 0x7f71e5a5b170> and will run it as-is.\n",
            "Cause: while/else statement not yet supported\n",
            "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
            "WARNING: AutoGraph could not transform <function wrap at 0x7f71e5a5b170> and will run it as-is.\n",
            "Cause: while/else statement not yet supported\n",
            "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
            "WARNING:tensorflow:The parameter `return_dict` cannot be set in graph mode and will always be set to `True`.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/array_ops.py:5049: calling gather (from tensorflow.python.ops.array_ops) with validate_indices is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "The `validate_indices` argument has no effect. Indices are always validated on CPU and never validated on GPU.\n",
            "WARNING:tensorflow:The parameters `output_attentions`, `output_hidden_states` and `use_cache` cannot be updated when calling a model.They have to be set to True/False in the config object (i.e.: `config=XConfig.from_pretrained('name', output_attentions=True)`).\n",
            "WARNING:tensorflow:The parameter `return_dict` cannot be set in graph mode and will always be set to `True`.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wal2MfhTCl3k",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ccb09030-3b3b-4994-f029-35b0938d644f"
      },
      "source": [
        "# Evaluate the model for measuring loss\n",
        "trainer.evaluate(test_dataset)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:The parameters `output_attentions`, `output_hidden_states` and `use_cache` cannot be updated when calling a model.They have to be set to True/False in the config object (i.e.: `config=XConfig.from_pretrained('name', output_attentions=True)`).\n",
            "WARNING:tensorflow:The parameter `return_dict` cannot be set in graph mode and will always be set to `True`.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'eval_loss': 0.2925073051452637}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZYtKkrHYhVhU"
      },
      "source": [
        "# Load the test dataset\n",
        "df_test = pd.read_csv('/content/drive/MyDrive/NLP/gold_test.csv')\n",
        "X_test=list(df_test['text'])\n",
        "y_test=list(df_test['is_humor'])\n",
        "\n",
        "# Encode the test dataset\n",
        "test_encodings = tokenizer(X_test, truncation=True, padding=True)\n",
        "\n",
        "# make testdataset using tensorflow\n",
        "test_dataset = tf.data.Dataset.from_tensor_slices((\n",
        "    dict(test_encodings),\n",
        "    y_test\n",
        "))"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cf445X7OCreY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "38b3ebaf-c8d6-46c7-ff90-58695dda08ff"
      },
      "source": [
        "# Test the model using test dataset\n",
        "output=trainer.predict(test_dataset)[1]"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:The parameters `output_attentions`, `output_hidden_states` and `use_cache` cannot be updated when calling a model.They have to be set to True/False in the config object (i.e.: `config=XConfig.from_pretrained('name', output_attentions=True)`).\n",
            "WARNING:tensorflow:The parameter `return_dict` cannot be set in graph mode and will always be set to `True`.\n",
            "WARNING:tensorflow:The parameters `output_attentions`, `output_hidden_states` and `use_cache` cannot be updated when calling a model.They have to be set to True/False in the config object (i.e.: `config=XConfig.from_pretrained('name', output_attentions=True)`).\n",
            "WARNING:tensorflow:The parameter `return_dict` cannot be set in graph mode and will always be set to `True`.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EPg5FSRKCvCW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1ecd1f80-6374-4ed3-842d-a4cd9c97d550"
      },
      "source": [
        "# Make confusion matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "cm=confusion_matrix(y_test,output)\n",
        "cm"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[385,   0],\n",
              "       [  0, 615]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HM2WLlFFHPqV"
      },
      "source": [
        "# Save the model for future use\n",
        "trainer.save_model('is_humor_model')"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kZcS5Arg8OdW"
      },
      "source": [
        "## **TASK1(B)** Humor rating"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_uw53-bR93Lv"
      },
      "source": [
        "# Read the train data for Humor Rating task\n",
        "df = pd.read_csv('/content/drive/MyDrive/NLP/train.csv')\n",
        "df['humor_rating'] = df['humor_rating'].fillna(0)\n",
        "df = df[(df.is_humor == 1)]"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q4s61MTn8c1R"
      },
      "source": [
        "# Train feature\n",
        "X_train_hr=list(df['text'])"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "trXfCce98jIR"
      },
      "source": [
        "# Train label\n",
        "y_train_hr=list(df['humor_rating'])"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MBvgy1al8kgX"
      },
      "source": [
        "# Spliting the data into training and testing the dataset\n",
        "X_train_hr, X_test_hr, y_train_hr, y_test_hr = train_test_split(X_train_hr, y_train_hr, test_size = 0.20, random_state = 0)"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cz8Pa3jb8qPy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 164,
          "referenced_widgets": [
            "63b837beb4d84a078d03700a577da7d0",
            "33f3c7697b7d4ad58ecbf3683f2e8eb9",
            "909521d42b04447db5164bf55bb5677d",
            "d31f356f2fa948b289dab30d84af5d79",
            "ee6a94a69de94b2fa2d727255c782eac",
            "86863ad2e79a489eacafef33f025a5b3",
            "06efa4e379f540999e1f6d9923eb27cc",
            "c1835623b415408e9015f042195f68ef",
            "9e8416637edd4798887f278ba0d98c17",
            "eebfa6123d15445498ffaa98761afb25",
            "d7f3c77d26804c4ba800743a959f4102",
            "85a29c9e9aec411494b2cd4a612586e9",
            "c33a449d5c72488883db39434fe62f2b",
            "a299661a304246508785b3a040dd7b96",
            "6f4af543dc9949bfba6eef6b89487e5a",
            "1376c53359df47ca9f99af09de4c556e",
            "d4c1d12650c7418f9f28f66a521b4053",
            "313d5f32714d487f887eeb6d070157d6",
            "85abfd3f795045a996d7d3429a96556e",
            "1c4ebed9df9f49bcbf8279c42aad3d46",
            "b9fbb802b01f4e5e847cfebee1f18efb",
            "8da23382c04244008d84a4fa8bff356a",
            "0d6a04f4d11f4e2782d04247cddb535a",
            "4cff6c035b5c4b58aeb89ca5b34e61a9"
          ]
        },
        "outputId": "901fd662-8438-4a43-c8ea-c9cc782f2121"
      },
      "source": [
        "# DistilBertTokenizerFast for tokenize the text\n",
        "tokenizer = DistilBertTokenizerFast.from_pretrained('distilbert-base-uncased')"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "63b837beb4d84a078d03700a577da7d0",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=231508.0, style=ProgressStyle(descripti…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9e8416637edd4798887f278ba0d98c17",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=466062.0, style=ProgressStyle(descripti…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d4c1d12650c7418f9f28f66a521b4053",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=28.0, style=ProgressStyle(description_w…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-yTqYwOA82UE"
      },
      "source": [
        "# Encode the text into id vector with truncation and padding\n",
        "train_encodings = tokenizer(X_train_hr, truncation=True, padding=True)\n",
        "test_encodings = tokenizer(X_test_hr, truncation=True, padding=True)"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lld0K7Gd85ot"
      },
      "source": [
        "# Making the training dataset using tensorflow\n",
        "train_dataset_hr = tf.data.Dataset.from_tensor_slices((\n",
        "    dict(train_encodings),\n",
        "    y_train_hr\n",
        "))\n",
        "\n",
        "test_dataset_hr = tf.data.Dataset.from_tensor_slices((\n",
        "    dict(test_encodings),\n",
        "    y_test_hr\n",
        "))"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UD0ytrg49Sil"
      },
      "source": [
        "# Making Training Argument for model training\n",
        "training_args_humor_rating = TFTrainingArguments(\n",
        "    output_dir='./results_humor_rating',          \n",
        "    num_train_epochs=2,              \n",
        "    per_device_train_batch_size=8,  \n",
        "    per_device_eval_batch_size=16,  \n",
        "    warmup_steps=500,               \n",
        "    weight_decay=0.01,              \n",
        "    logging_dir='./logs_humor_rating',\n",
        "    logging_steps=10,\n",
        "    eval_steps=64\n",
        ")"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PWNm9_v49UZc",
        "outputId": "d555b6fc-80cc-43bf-905a-052644137798"
      },
      "source": [
        "# Use 'distilbert-base-uncased' as pre-trained model to get weights \n",
        "with training_args_humor_rating.strategy.scope():\n",
        "    model = TFDistilBertForSequenceClassification.from_pretrained(\"distilbert-base-uncased\",num_labels = 1)\n",
        "\n",
        "trainer = TFTrainer(\n",
        "    model=model,                         \n",
        "    args=training_args_humor_rating,     \n",
        "    train_dataset=train_dataset_hr,      \n",
        "    eval_dataset=test_dataset_hr         \n",
        ")\n",
        "\n",
        "trainer.train()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Some layers from the model checkpoint at distilbert-base-uncased were not used when initializing TFDistilBertForSequenceClassification: ['activation_13', 'vocab_layer_norm', 'vocab_transform', 'vocab_projector']\n",
            "- This IS expected if you are initializing TFDistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing TFDistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some layers of TFDistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['pre_classifier', 'classifier', 'dropout_39']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/trainer_tf.py:114: FutureWarning: The class `TFTrainer` is deprecated and will be removed in version 5 of Transformers. We recommend using native Keras instead, by calling methods like `fit()` and `predict()` directly on the model object. Detailed examples of the Keras style can be found in our examples at https://github.com/huggingface/transformers/tree/master/examples/tensorflow\n",
            "  FutureWarning,\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:The parameters `output_attentions`, `output_hidden_states` and `use_cache` cannot be updated when calling a model.They have to be set to True/False in the config object (i.e.: `config=XConfig.from_pretrained('name', output_attentions=True)`).\n",
            "WARNING:tensorflow:The parameter `return_dict` cannot be set in graph mode and will always be set to `True`.\n",
            "WARNING:tensorflow:The parameters `output_attentions`, `output_hidden_states` and `use_cache` cannot be updated when calling a model.They have to be set to True/False in the config object (i.e.: `config=XConfig.from_pretrained('name', output_attentions=True)`).\n",
            "WARNING:tensorflow:The parameter `return_dict` cannot be set in graph mode and will always be set to `True`.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yp8hsseC9WHD",
        "outputId": "b5386d96-18ef-48eb-bef2-39915b108791"
      },
      "source": [
        "# Evaluate the model for measuring loss\n",
        "trainer.evaluate(test_dataset_hr)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:The parameters `output_attentions`, `output_hidden_states` and `use_cache` cannot be updated when calling a model.They have to be set to True/False in the config object (i.e.: `config=XConfig.from_pretrained('name', output_attentions=True)`).\n",
            "WARNING:tensorflow:The parameter `return_dict` cannot be set in graph mode and will always be set to `True`.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'eval_loss': 0.017048018593941967}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dq9vaCMz9YVr"
      },
      "source": [
        "# Load the test dataset\n",
        "df_test = pd.read_csv('/content/drive/MyDrive/NLP/gold_test.csv')\n",
        "df_test['humor_rating'] = df_test['humor_rating'].fillna(0)\n",
        "X_test=list(df_test['text'])\n",
        "y_test=list(df_test['humor_rating'])\n",
        "\n",
        "# Encode the test dataset\n",
        "test_encodings = tokenizer(X_test, truncation=True, padding=True)\n",
        "\n",
        "# make testdataset using tensorflow\n",
        "test_dataset = tf.data.Dataset.from_tensor_slices((\n",
        "    dict(test_encodings),\n",
        "    y_test\n",
        "))"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NBHrwPwd9aee",
        "outputId": "cb947128-7644-42d8-cf72-5276daf85c5d"
      },
      "source": [
        "# Test the model using test dataset\n",
        "output=trainer.predict(test_dataset)[1]"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:The parameters `output_attentions`, `output_hidden_states` and `use_cache` cannot be updated when calling a model.They have to be set to True/False in the config object (i.e.: `config=XConfig.from_pretrained('name', output_attentions=True)`).\n",
            "WARNING:tensorflow:The parameter `return_dict` cannot be set in graph mode and will always be set to `True`.\n",
            "WARNING:tensorflow:The parameters `output_attentions`, `output_hidden_states` and `use_cache` cannot be updated when calling a model.They have to be set to True/False in the config object (i.e.: `config=XConfig.from_pretrained('name', output_attentions=True)`).\n",
            "WARNING:tensorflow:The parameter `return_dict` cannot be set in graph mode and will always be set to `True`.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gp2x9n1o9b3X",
        "outputId": "ae2c5ecc-9821-4eb2-bcf0-e510f82a62d6"
      },
      "source": [
        "# Calculate MSE\n",
        "diff = 0\n",
        "for i in range(output.shape[0]):\n",
        "  if(output[i] != 'nan' or y_test[i]):\n",
        "    diff += (abs(y_test[i] - output[i])**2)\n",
        "\n",
        "print(diff/len(y_test))"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.9739289303990405e-15\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ivUuI8Zq9cXZ"
      },
      "source": [
        "# Save the model for future use\n",
        "trainer.save_model('humor__rating_model')"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "74K1FYHzChsJ"
      },
      "source": [
        "## **TASK1(C)** Humor controversy detection"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Nov_j5tCg9P"
      },
      "source": [
        "# Read the train data for Humor Controversy task and preprocess with imputing 0 in null cells\n",
        "df = pd.read_csv('/content/drive/MyDrive/NLP/train.csv')\n",
        "df['humor_controversy'] = df['humor_controversy'].fillna(0)\n",
        "df['humor_controversy'] = df['humor_controversy'].apply(np.int64)\n",
        "df = df[(df.is_humor == 1)]"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KHHAYs8CCrN5"
      },
      "source": [
        "# Train feature\n",
        "X_train = list(df['text'])"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3CdaRtaYCulQ"
      },
      "source": [
        "# Train label\n",
        "y_train=list(df['humor_controversy'])"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2i9KNrjQCv5f"
      },
      "source": [
        "# Spliting the data into training and testing the dataset\n",
        "X_train_hc, X_test_hc, y_train_hc, y_test_hc = train_test_split(X_train, y_train, test_size = 0.20, random_state = 0)"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TPvuLqEJC38-"
      },
      "source": [
        "# DistilBertTokenizerFast for tokenize the text\n",
        "tokenizer = DistilBertTokenizerFast.from_pretrained('distilbert-base-uncased')"
      ],
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6u-XY5gUC5Y0"
      },
      "source": [
        "# Encode the text into id vector with truncation and padding\n",
        "train_encodings = tokenizer(X_train_hc, truncation=True, padding=True)\n",
        "test_encodings = tokenizer(X_test_hc, truncation=True, padding=True)"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zdYwchl_C7n-"
      },
      "source": [
        "# Making the training dataset using tensorflow\n",
        "train_dataset_hc = tf.data.Dataset.from_tensor_slices((\n",
        "    dict(train_encodings),\n",
        "    y_train_hc\n",
        "))\n",
        "\n",
        "test_dataset_hc = tf.data.Dataset.from_tensor_slices((\n",
        "    dict(test_encodings),\n",
        "    y_test_hc\n",
        "))"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U8lJhnu5C9ZE"
      },
      "source": [
        "# Making Training Argument for model training\n",
        "training_args_hc = TFTrainingArguments(\n",
        "    output_dir='./results_humor_controversy',          \n",
        "    num_train_epochs=2,             \n",
        "    per_device_train_batch_size=8,  \n",
        "    per_device_eval_batch_size=16,  \n",
        "    warmup_steps=500,               \n",
        "    weight_decay=0.01,              \n",
        "    logging_dir='./logs_humor_controversy',\n",
        "    logging_steps=10,\n",
        "    eval_steps=16\n",
        ")"
      ],
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9QxDbyMcC_Jt",
        "outputId": "76aeece3-b36a-4993-c020-d98ae60bf784"
      },
      "source": [
        "# Use 'distilbert-base-uncased' as pre-trained model to get weights \n",
        "with training_args_hc.strategy.scope():\n",
        "    model = TFDistilBertForSequenceClassification.from_pretrained(\"distilbert-base-uncased\")\n",
        "\n",
        "trainer = TFTrainer(\n",
        "    model=model,                         \n",
        "    args=training_args_hc,               \n",
        "    train_dataset=train_dataset_hc,      \n",
        "    eval_dataset=test_dataset_hc         \n",
        ")\n",
        "\n",
        "trainer.train()"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Some layers from the model checkpoint at distilbert-base-uncased were not used when initializing TFDistilBertForSequenceClassification: ['activation_13', 'vocab_layer_norm', 'vocab_transform', 'vocab_projector']\n",
            "- This IS expected if you are initializing TFDistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing TFDistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some layers of TFDistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['pre_classifier', 'dropout_59', 'classifier']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/trainer_tf.py:114: FutureWarning: The class `TFTrainer` is deprecated and will be removed in version 5 of Transformers. We recommend using native Keras instead, by calling methods like `fit()` and `predict()` directly on the model object. Detailed examples of the Keras style can be found in our examples at https://github.com/huggingface/transformers/tree/master/examples/tensorflow\n",
            "  FutureWarning,\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:The parameters `output_attentions`, `output_hidden_states` and `use_cache` cannot be updated when calling a model.They have to be set to True/False in the config object (i.e.: `config=XConfig.from_pretrained('name', output_attentions=True)`).\n",
            "WARNING:tensorflow:The parameter `return_dict` cannot be set in graph mode and will always be set to `True`.\n",
            "WARNING:tensorflow:The parameters `output_attentions`, `output_hidden_states` and `use_cache` cannot be updated when calling a model.They have to be set to True/False in the config object (i.e.: `config=XConfig.from_pretrained('name', output_attentions=True)`).\n",
            "WARNING:tensorflow:The parameter `return_dict` cannot be set in graph mode and will always be set to `True`.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PYzNVrZtDBOj",
        "outputId": "adcd5f4b-c871-4af2-98b8-8ccb7307e68a"
      },
      "source": [
        "# Evaluate the model for measuring loss\n",
        "trainer.evaluate(test_dataset_hc)"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:The parameters `output_attentions`, `output_hidden_states` and `use_cache` cannot be updated when calling a model.They have to be set to True/False in the config object (i.e.: `config=XConfig.from_pretrained('name', output_attentions=True)`).\n",
            "WARNING:tensorflow:The parameter `return_dict` cannot be set in graph mode and will always be set to `True`.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'eval_loss': 0.7450577520555065}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ybLtoF29DDex"
      },
      "source": [
        "# Load the test dataset\n",
        "df_test = pd.read_csv('/content/drive/MyDrive/NLP/gold_test.csv')\n",
        "df_test['humor_controversy'] = df_test['humor_controversy'].fillna(0)\n",
        "df_test['humor_controversy'] = df_test['humor_controversy'].apply(np.int64)\n",
        "X_test=list(df_test['text'])\n",
        "y_test=list(df_test['humor_controversy'])\n",
        "\n",
        "# Encode the test dataset\n",
        "test_encodings = tokenizer(X_test, truncation=True, padding=True)\n",
        "\n",
        "# make testdataset using tensorflow\n",
        "test_dataset = tf.data.Dataset.from_tensor_slices((\n",
        "    dict(test_encodings),\n",
        "    y_test\n",
        "))"
      ],
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bhId1saDDE_K",
        "outputId": "cef7ae05-b465-4026-d863-f65cb6869809"
      },
      "source": [
        "# Test the model using test dataset\n",
        "output=trainer.predict(test_dataset)[1]"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:The parameters `output_attentions`, `output_hidden_states` and `use_cache` cannot be updated when calling a model.They have to be set to True/False in the config object (i.e.: `config=XConfig.from_pretrained('name', output_attentions=True)`).\n",
            "WARNING:tensorflow:The parameter `return_dict` cannot be set in graph mode and will always be set to `True`.\n",
            "WARNING:tensorflow:The parameters `output_attentions`, `output_hidden_states` and `use_cache` cannot be updated when calling a model.They have to be set to True/False in the config object (i.e.: `config=XConfig.from_pretrained('name', output_attentions=True)`).\n",
            "WARNING:tensorflow:The parameter `return_dict` cannot be set in graph mode and will always be set to `True`.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6NopnR5KDIcO",
        "outputId": "f3fd581f-351e-47b2-9be6-4def2e3626ac"
      },
      "source": [
        "# Make confusion matrix\n",
        "cm=confusion_matrix(y_test,output)\n",
        "cm"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[721,   0],\n",
              "       [  0, 279]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "srzzaP1lDKLo"
      },
      "source": [
        "# Save the model for future use\n",
        "trainer.save_model('humor_controversy_model')"
      ],
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0a-LiVJOGpzd"
      },
      "source": [
        "## **TASK2** Humor offense rating"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xtnQHfLUGlya"
      },
      "source": [
        "# Read the train data for Humor Offense Rating task\n",
        "df = pd.read_csv('/content/drive/MyDrive/NLP/train.csv')\n",
        "df = df[(df.is_humor == 1)]"
      ],
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_hwkPaS3G6yE"
      },
      "source": [
        "# Train feature\n",
        "X_train = list(df['text'])"
      ],
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XEAt7N3rG_Ls"
      },
      "source": [
        "# Train label\n",
        "y_train=list(df['offense_rating'])"
      ],
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iWNrC6p4HEt2"
      },
      "source": [
        "# Spliting the data into training and testing the dataset\n",
        "X_train_or, X_test_or, y_train_or, y_test_or = train_test_split(X_train, y_train, test_size = 0.20, random_state = 0)"
      ],
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cHbKtr6gHLQg"
      },
      "source": [
        "# DistilBertTokenizerFast for tokenize the text\n",
        "tokenizer = DistilBertTokenizerFast.from_pretrained('distilbert-base-uncased')"
      ],
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YGUBTzU7HL1h"
      },
      "source": [
        "# Encode the text into id vector with truncation and padding\n",
        "train_encodings = tokenizer(X_train_or, truncation=True, padding=True)\n",
        "test_encodings = tokenizer(X_test_or, truncation=True, padding=True)"
      ],
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TXdJkBlGHUhA"
      },
      "source": [
        "# Making the training dataset using tensorflow\n",
        "train_dataset_or = tf.data.Dataset.from_tensor_slices((\n",
        "    dict(train_encodings),\n",
        "    y_train_or\n",
        "))\n",
        "\n",
        "test_dataset_or = tf.data.Dataset.from_tensor_slices((\n",
        "    dict(test_encodings),\n",
        "    y_test_or\n",
        "))"
      ],
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X0pA5zktHbSJ"
      },
      "source": [
        "# Making Training Argument for model training\n",
        "training_args_or = TFTrainingArguments(\n",
        "    output_dir='./results_Offense_Rating',          \n",
        "    num_train_epochs=2,              \n",
        "    per_device_train_batch_size=8,  \n",
        "    per_device_eval_batch_size=16,   \n",
        "    warmup_steps=500,                \n",
        "    weight_decay=0.01,               \n",
        "    logging_dir='./logs_Offense_Rating',\n",
        "    logging_steps=10,\n",
        "    eval_steps=16\n",
        ")"
      ],
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b8IZXOnDHjZ-",
        "outputId": "6cfb2091-cf17-432b-c0ea-06a074bf197f"
      },
      "source": [
        "# Use 'distilbert-base-uncased' as pre-trained model to get weights \n",
        "with training_args_or.strategy.scope():\n",
        "    model = TFDistilBertForSequenceClassification.from_pretrained(\"distilbert-base-uncased\")\n",
        "\n",
        "trainer = TFTrainer(\n",
        "    model=model,                         \n",
        "    args=training_args_or,          \n",
        "    train_dataset=train_dataset_or,    \n",
        "    eval_dataset=test_dataset_or       \n",
        ")\n",
        "\n",
        "trainer.train()"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Some layers from the model checkpoint at distilbert-base-uncased were not used when initializing TFDistilBertForSequenceClassification: ['activation_13', 'vocab_layer_norm', 'vocab_transform', 'vocab_projector']\n",
            "- This IS expected if you are initializing TFDistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing TFDistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some layers of TFDistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['pre_classifier', 'classifier', 'dropout_79']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/trainer_tf.py:114: FutureWarning: The class `TFTrainer` is deprecated and will be removed in version 5 of Transformers. We recommend using native Keras instead, by calling methods like `fit()` and `predict()` directly on the model object. Detailed examples of the Keras style can be found in our examples at https://github.com/huggingface/transformers/tree/master/examples/tensorflow\n",
            "  FutureWarning,\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:The parameters `output_attentions`, `output_hidden_states` and `use_cache` cannot be updated when calling a model.They have to be set to True/False in the config object (i.e.: `config=XConfig.from_pretrained('name', output_attentions=True)`).\n",
            "WARNING:tensorflow:The parameter `return_dict` cannot be set in graph mode and will always be set to `True`.\n",
            "WARNING:tensorflow:The parameters `output_attentions`, `output_hidden_states` and `use_cache` cannot be updated when calling a model.They have to be set to True/False in the config object (i.e.: `config=XConfig.from_pretrained('name', output_attentions=True)`).\n",
            "WARNING:tensorflow:The parameter `return_dict` cannot be set in graph mode and will always be set to `True`.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YKHKXdX8Hng6",
        "outputId": "fb7b2dae-2881-4e13-f189-dcc42eeb4b29"
      },
      "source": [
        "# Evaluate the model for measuring loss\n",
        "trainer.evaluate(test_dataset_or)"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:The parameters `output_attentions`, `output_hidden_states` and `use_cache` cannot be updated when calling a model.They have to be set to True/False in the config object (i.e.: `config=XConfig.from_pretrained('name', output_attentions=True)`).\n",
            "WARNING:tensorflow:The parameter `return_dict` cannot be set in graph mode and will always be set to `True`.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'eval_loss': nan}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6rgFjqqgH7Sv"
      },
      "source": [
        "# Load the test dataset\n",
        "df_test = pd.read_csv('/content/drive/MyDrive/NLP/gold_test.csv')\n",
        "df_test['humor_controversy'] = df_test['humor_controversy'].fillna(0)\n",
        "df_test['humor_controversy'] = df_test['humor_controversy'].apply(np.int64)\n",
        "X_test=list(df_test['text'])\n",
        "y_test=list(df_test['offense_rating'])\n",
        "\n",
        "# Encode the test dataset\n",
        "test_encodings = tokenizer(X_test, truncation=True, padding=True)\n",
        "\n",
        "# make testdataset using tensorflow\n",
        "test_dataset = tf.data.Dataset.from_tensor_slices((\n",
        "    dict(test_encodings),\n",
        "    y_test\n",
        "))"
      ],
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bB9Hkr7jH97e",
        "outputId": "b99e341e-ef8e-4ab1-b38c-42525ae6b0f5"
      },
      "source": [
        "# Test the model using test dataset\n",
        "output=trainer.predict(test_dataset)[1]"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:The parameters `output_attentions`, `output_hidden_states` and `use_cache` cannot be updated when calling a model.They have to be set to True/False in the config object (i.e.: `config=XConfig.from_pretrained('name', output_attentions=True)`).\n",
            "WARNING:tensorflow:The parameter `return_dict` cannot be set in graph mode and will always be set to `True`.\n",
            "WARNING:tensorflow:The parameters `output_attentions`, `output_hidden_states` and `use_cache` cannot be updated when calling a model.They have to be set to True/False in the config object (i.e.: `config=XConfig.from_pretrained('name', output_attentions=True)`).\n",
            "WARNING:tensorflow:The parameter `return_dict` cannot be set in graph mode and will always be set to `True`.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "59qyrkrOICOk",
        "outputId": "64a4bad2-9219-46dd-d0b2-e0d609c24c55"
      },
      "source": [
        "# Calculate MSE\n",
        "diff = 0\n",
        "for i in range(output.shape[0]):\n",
        "  diff += (abs(y_test[i] - output[i])**2)\n",
        "\n",
        "print(diff/len(y_test))"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "5.770972587808648e-16\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "klMPeB6TIDz1"
      },
      "source": [
        "# Save the model for future use\n",
        "trainer.save_model('offence_rating_model')"
      ],
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UFwNGMXTSit1"
      },
      "source": [
        "## References:\n",
        "\n",
        "- https://huggingface.co/transformers/training.html\n",
        "- https://youtu.be/V1-Hm2rNkik\n",
        "- https://huggingface.co/transformers/model_doc/distilbert.html\n"
      ]
    }
  ]
}